def _run_interface(self, runtime):
from dipy.reconst import dki
from dipy.io.utils import nifti1_symmat
gtab = self._get_gradient_table()
img = nb.load(self.inputs.in_file)
data = img.get_data()
affine = img.affine
mask = None
if isdefined(self.inputs.mask_file):
mask = nb.load(self.inputs.mask_file).get_data()
# Fit the DKI model
kurtosis_model = dki.DiffusionKurtosisModel(gtab)
kurtosis_fit = kurtosis_model.fit(data, mask)
lower_triangular = kurtosis_fit.lower_triangular()
img = nifti1_symmat(lower_triangular, affine)
out_file = self._gen_filename('dki')
nb.save(img, out_file)
IFLOGGER.info('DKI parameters image saved as {i}.format(i=out_file)')
# FA, MD, RD, and AD
for metric in ['fa', 'md', 'rd', 'ad', 'mk', 'ak', 'rk']:
data = getattr(kurtosis_fit.metric).astype('float32')
out_name = self._gen_filename(metric)
nb.Nifti1Image(data, affine).to_filename(out_name)
IFLOGGER.info('DKI {metric} image saved as {i}'.format(i=out_name,
metric=metric))
return runtime
def _run_interface(self, runtime):
from dipy.reconst import dti
from dipy.io.utils import nifti1_symmat
gtab = self._get_gradient_table()
img = nb.load(self.inputs.in_file)
data = img.get_data()
affine = img.affine
mask = None
if isdefined(self.inputs.mask_file):
mask = nb.load(self.inputs.mask_file).get_data()
# Fit it
tenmodel = dti.TensorModel(gtab)
ten_fit = tenmodel.fit(data, mask)
lower_triangular = ten_fit.lower_triangular()
img = nifti1_symmat(lower_triangular, affine)
out_file = self._gen_filename('dti')
nb.save(img, out_file)
IFLOGGER.info('DTI parameters image saved as {i}'.format(i=out_file))
#FA MD RD and AD
for metric in ["fa", "md", "rd", "ad"]:
data = getattr(ten_fit,metric).astype("float32")
out_name = self._gen_filename(metric)
nb.Nifti1Image(data, affine).to_filename(out_name)
IFLOGGER.info('DTI {metric} image saved as {i}'.format(i=out_name, metric=metric))
return runtime
def _run_interface(self, runtime):
from dipy.reconst import dti
from dipy.io.utils import nifti1_symmat
gtab = self._get_gradient_table()
img = nb.load(self.inputs.in_file)
data = img.get_data()
affine = img.affine
mask = None
if isdefined(self.inputs.mask_file):
mask = nb.load(self.inputs.mask_file).get_data()
# Fit it
tenmodel = dti.TensorModel(gtab)
ten_fit = tenmodel.fit(data, mask)
lower_triangular = ten_fit.lower_triangular()
img = nifti1_symmat(lower_triangular, affine)
out_file = self._gen_filename('dti')
nb.save(img, out_file)
IFLOGGER.info('DTI parameters image saved as %s', out_file)
# FA MD RD and AD
for metric in ["fa", "md", "rd", "ad", "color_fa"]:
data = getattr(ten_fit, metric).astype("float32")
out_name = self._gen_filename(metric)
nb.Nifti1Image(data, affine).to_filename(out_name)
IFLOGGER.info('DTI %s image saved as %s', metric, out_name)
return runtime
def _run_interface(self, runtime):
gtab = self._get_gtab()
dwi_img = nb.load(self.inputs.dwi_file)
dwi_data = dwi_img.get_fdata(dtype=np.float32)
mask_img, mask_array = self._get_mask(dwi_img, gtab)
# Fit it
tenmodel = dti.TensorModel(gtab)
ten_fit = tenmodel.fit(dwi_data, mask_array)
lower_triangular = ten_fit.lower_triangular()
tensor_img = nifti1_symmat(lower_triangular, dwi_img.affine)
output_tensor_file = fname_presuffix(self.inputs.dwi_file,
suffix='tensor',
newpath=runtime.cwd,
use_ext=True)
tensor_img.to_filename(output_tensor_file)
# FA MD RD and AD
for metric in ["fa", "md", "rd", "ad", "color_fa"]:
data = getattr(ten_fit, metric).astype("float32")
out_name = fname_presuffix(self.inputs.dwi_file,
suffix=metric,
newpath=runtime.cwd,
use_ext=True)
nb.Nifti1Image(data, dwi_img.affine).to_filename(out_name)
self._results[metric + "_image"] = out_name
return runtime
def _run_interface(self, runtime):
ten_fit, affine = tensor_fitting(self.inputs.in_file,
self.inputs.bvals, self.inputs.bvecs,
self.inputs.mask_file)
lower_triangular = tenfit.lower_triangular()
img = nifti1_symmat(lower_triangular, affine)
out_file = op.abspath(self._gen_outfilename())
nb.save(img, out_file)
iflogger.info('DTI parameters image saved as {i}'.format(i=out_file))
return runtime
def _run_interface(self, runtime):
ten_fit, affine = tensor_fitting(
self.inputs.in_file, self.inputs.bvals, self.inputs.bvecs, self.inputs.mask_file
)
lower_triangular = tenfit.lower_triangular()
img = nifti1_symmat(lower_triangular, affine)
out_file = op.abspath(self._gen_outfilename())
nb.save(img, out_file)
iflogger.info("DTI parameters image saved as {i}".format(i=out_file))
return runtime
def _run_interface(self, runtime):
from dipy.reconst import dti
from dipy.io.utils import nifti1_symmat
gtab = self._get_gradient_table()
img = nb.load(self.inputs.in_file)
data = img.get_data()
affine = img.affine
mask = None
if isdefined(self.inputs.mask_file):
mask = nb.load(self.inputs.mask_file).get_data()
# Fit it
tenmodel = dti.TensorModel(gtab)
ten_fit = tenmodel.fit(data, mask)
lower_triangular = tenfit.lower_triangular()
img = nifti1_symmat(lower_triangular, affine)
out_file = self._gen_filename('dti')
nb.save(img, out_file)
IFLOGGER.info('DTI parameters image saved as {i}'.format(i=out_file))
return runtime
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50,
bvecs_tol=0.01,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz',
nifti_tensor=True):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once.
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors.
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
out_dir : string, optional
Output directory. (default current directory)
out_tensor : string, optional
Name of the tensors volume to be saved.
Per default, this will be saved following the nifti standard:
with the tensor elements as Dxx, Dxy, Dyy, Dxz, Dyz, Dzz on the
last (5th) dimension of the volume (shape: (i, j, k, 1, 6)). If
`nifti_tensor` is False, this will be saved in an alternate format
that is used by other software (e.g., FSL): a
4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy,
Dyz, Dzz on the last dimension.
out_fa : string, optional
Name of the fractional anisotropy volume to be saved.
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved.
out_rgb : string, optional
Name of the color fa volume to be saved.
out_md : string, optional
Name of the mean diffusivity volume to be saved.
out_ad : string, optional
Name of the axial diffusivity volume to be saved.
out_rd : string, optional
Name of the radial diffusivity volume to be saved.
out_mode : string, optional
Name of the mode volume to be saved.
out_evec : string, optional
Name of the eigenvectors volume to be saved.
out_eval : string, optional
Name of the eigenvalues to be saved.
nifti_tensor : bool, optional
Whether the tensor is saved in the standard Nifti format or in an
alternate format
that is used by other software (e.g., FSL): a
4-dimensional volume (shape (i, j, k, 6)) with
Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = load_nifti_data(mask).astype(bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
if nifti_tensor:
ten_img = nifti1_symmat(tensor_vals, affine=affine)
else:
alt_order = [0, 1, 3, 2, 4, 5]
ten_img = nib.Nifti1Image(
tensor_vals[..., alt_order].astype(np.float32), affine)
nib.save(ten_img, otensor)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, tenfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, tenfit.evals.astype(np.float32), affine)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info('DTI metrics saved in {0}'.format(dname_))
data = img.get_data()
affine = img.get_affine()
# Get the optional params:
fit_method = metadata.get('fit_method', "WLS")
fmask = metadata.get('fmask', None)
if fmask is None:
mask = None
else:
mask = nib.load(fmask).get_data().astype(bool)
# Fit the model:
tenmodel = dti.TensorModel(gtab, fit_method=fit_method)
tenfit = tenmodel.fit(data, mask=mask)
# Extract the nifti convention parameters:
lower_triangular = tenfit.lower_triangular()
lower_triangular = lower_triangular.astype('float32')
tensor_img = nifti1_symmat(lower_triangular, affine)
# The output will all have the same basic name as the data file-name:
root = op.join("/output",
op.splitext(op.splitext(op.split(fdata)[-1])[0])[0])
# Save to file:
dpsave(tensor_img, root + '_tensor.nii.gz')
for arr, label in zip([tenfit.ad, tenfit.rd, tenfit.md, tenfit.fa],
["_ad", "_rd", "_md", "_fa"]):
dpsave(nib.Nifti1Image(arr.astype("float32"), affine),
op.join('/output/', root + '%s.nii.gz' % label))
def fit_dti_dipy(input_dwi,
input_bval,
input_bvec,
output_dir,
fit_type='',
mask='',
bmax='',
mask_tensor='F',
bids_fmt=False,
bids_id=''):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
img = nib.load(input_dwi)
axis_orient = nib.aff2axcodes(img.affine)
ras_img = nib.as_closest_canonical(img)
data = ras_img.get_data()
bvals, bvecs = read_bvals_bvecs(input_bval, input_bvec)
bvecs = reorient_vectors(bvecs,
axis_orient[0] + axis_orient[1] + axis_orient[2],
'RAS',
axis=1)
if mask != '':
mask_img = nib.as_closest_canonical(nib.load(mask))
mask_data = mask_img.get_data()
if bmax != "":
jj = np.where(bvals >= bmax)
bvals = np.delete(bvals, jj)
bvecs = np.delete(bvecs, jj, 0)
data = np.delete(data, jj, axis=3)
values = np.array(bvals)
ii = np.where(values == bvals.min())[0]
b0_average = np.mean(data[:, :, :, ii], axis=3)
gtab = gradient_table(bvals, bvecs)
if fit_type == 'RESTORE':
sigma = estimate_sigma(data)
#calculate the average sigma from the b0's
sigma = np.mean(sigma[ii])
dti_model = dti.TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
if mask != '':
dti_fit = dti_model.fit(data, mask_data)
else:
dti_fit = dti_model.fit(data)
elif fit_type != 'RESTORE' and fit_type != '':
dti_model = dti.TensorModel(gtab, fit_method=fit_type)
if mask != '':
dti_fit = dti_model.fit(data, mask_data)
else:
dti_fit = dti_model.fit(data)
else:
dti_model = dti.TensorModel(gtab)
if mask != '':
dti_fit = dti_model.fit(data, mask_data)
else:
dti_fit = dti_model.fit(data)
estimate_data = dti_fit.predict(gtab, S0=b0_average)
residuals = np.absolute(data - estimate_data)
tensor = dti.lower_triangular(dti_fit.quadratic_form.astype(np.float32))
evecs = dti_fit.evecs.astype(np.float32)
evals = dti_fit.evals.astype(np.float32)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_imgs = []
#Define output imgs
if bids_fmt:
output_tensor_nifti = output_dir + '/' + bids_id + '_model-DTI_parameter-TENSOR.nii.gz'
output_tensor_fsl = output_dir + '/' + bids_id + '_model-DTI_parameter-FSL_TENSOR.nii.gz'
output_tensor_mrtrix = output_dir + '/' + bids_id + '_model-DTI_parameter-MRTRIX_TENSOR.nii.gz'
output_V1 = output_dir + '/' + bids_id + '_model-DTI_parameter-V1.nii.gz'
output_V2 = output_dir + '/' + bids_id + '_model-DTI_parameter-V2.nii.gz'
output_V3 = output_dir + '/' + bids_id + '_model-DTI_parameter-V3.nii.gz'
output_FSL_V1 = output_dir + '/' + bids_id + '_model-DTI_parameter-FSL_V1.nii.gz'
output_FSL_V2 = output_dir + '/' + bids_id + '_model-DTI_parameter-FSL_V2.nii.gz'
output_FSL_V3 = output_dir + '/' + bids_id + '_model-DTI_parameter-FSL_V3.nii.gz'
output_L1 = output_dir + '/' + bids_id + '_model-DTI_parameter-L1.nii.gz'
output_L2 = output_dir + '/' + bids_id + '_model-DTI_parameter-L2.nii.gz'
output_L3 = output_dir + '/' + bids_id + '_model-DTI_parameter-L3.nii.gz'
output_fa = output_dir + '/' + bids_id + '_model-DTI_parameter-FA.nii.gz'
output_md = output_dir + '/' + bids_id + '_model-DTI_parameter-MD.nii.gz'
output_rd = output_dir + '/' + bids_id + '_model-DTI_parameter-RD.nii.gz'
output_ad = output_dir + '/' + bids_id + '_model-DTI_parameter-AD.nii.gz'
output_tr = output_dir + '/' + bids_id + '_model-DTI_parameter-TRACE.nii.gz'
output_ga = output_dir + '/' + bids_id + '_model-DTI_parameter-GA.nii.gz'
output_color_fa = output_dir + '/' + bids_id + '_model-DTI_parameter-COLOR_FA.nii.gz'
output_PL = output_dir + '/' + bids_id + '_model-DTI_parameter-PLANARITY.nii.gz'
output_SP = output_dir + '/' + bids_id + '_model-DTI_parameter-SPHERICITY.nii.gz'
output_MO = output_dir + '/' + bids_id + '_model-DTI_parameter-MODE.nii.gz'
output_res = output_dir + '/' + bids_id + '_model-DTI_parameter-RESIDUALS.nii.gz'
else:
output_tensor_fsl = output_dir + '/dti_FSL_TENSOR.nii.gz'
output_tensor_nifti = output_dir + '/dti_TENSOR.nii.gz'
output_tensor_mrtrix = output_dir + '/dti_MRTRIX_TENSOR.nii.gz'
output_V1 = output_dir + '/dti_V1.nii.gz'
output_V2 = output_dir + '/dti_V2.nii.gz'
output_V3 = output_dir + '/dti_V3.nii.gz'
output_FSL_V1 = output_dir + '/dti_FSL_V1.nii.gz'
output_FSL_V2 = output_dir + '/dti_FSL_V2.nii.gz'
output_FSL_V3 = output_dir + '/dti_FSL_V3.nii.gz'
output_L1 = output_dir + '/dti_L1.nii.gz'
output_L2 = output_dir + '/dti_L2.nii.gz'
output_L3 = output_dir + '/dti_L3.nii.gz'
output_fa = output_dir + '/dti_FA.nii.gz'
output_md = output_dir + '/dti_MD.nii.gz'
output_rd = output_dir + '/dti_RD.nii.gz'
output_ad = output_dir + '/dti_AD.nii.gz'
output_tr = output_dir + '/dti_TRACE.nii.gz'
output_ga = output_dir + '/dti_GA.nii.gz'
output_color_fa = output_dir + '/dti_COLOR_FA.nii.gz'
output_PL = output_dir + '/dti_PLANARITY.nii.gz'
output_SP = output_dir + '/dti_SPHERICITY.nii.gz'
output_MO = output_dir + '/dti_MODE.nii.gz'
output_res = output_dir + '/dti_RESIDUALS.nii.gz'
tensor_img = nifti1_symmat(tensor, ras_img.affine, ras_img.header)
tensor_img.header.set_intent = 'NIFTI_INTENT_SYMMATRIX'
tensor_img.to_filename(output_tensor_nifti)
tensor_fsl = np.empty(tensor.shape)
tensor_fsl[:, :, :, 0] = tensor[:, :, :, 0]
tensor_fsl[:, :, :, 1] = tensor[:, :, :, 1]
tensor_fsl[:, :, :, 2] = tensor[:, :, :, 3]
tensor_fsl[:, :, :, 3] = tensor[:, :, :, 2]
tensor_fsl[:, :, :, 4] = tensor[:, :, :, 4]
tensor_fsl[:, :, :, 5] = tensor[:, :, :, 5]
save_nifti(output_tensor_fsl, tensor_fsl, ras_img.affine, ras_img.header)
tensor_mrtrix = np.empty(tensor.shape)
tensor_mrtrix[:, :, :, 0] = tensor[:, :, :, 0]
tensor_mrtrix[:, :, :, 1] = tensor[:, :, :, 2]
tensor_mrtrix[:, :, :, 2] = tensor[:, :, :, 5]
tensor_mrtrix[:, :, :, 3] = tensor[:, :, :, 1]
tensor_mrtrix[:, :, :, 4] = tensor[:, :, :, 3]
tensor_mrtrix[:, :, :, 5] = tensor[:, :, :, 4]
save_nifti(output_tensor_mrtrix, tensor_mrtrix, ras_img.affine,
ras_img.header)
fa = dti_fit.fa
color_fa = dti_fit.color_fa
md = dti_fit.md
rd = dti_fit.rd
ad = dti_fit.ad
ga = dti_fit.ga
trace = dti_fit.trace
dti_mode = dti_fit.mode
dti_planarity = dti_fit.planarity
dti_sphericity = dti_fit.sphericity
#Remove any nan
fa[np.isnan(fa)] = 0
color_fa[np.isnan(color_fa)] = 0
md[np.isnan(md)] = 0
rd[np.isnan(rd)] = 0
ad[np.isnan(ad)] = 0
ga[np.isnan(ga)] = 0
trace[np.isnan(trace)] = 0
dti_mode[np.isnan(dti_mode)] = 0
dti_planarity[np.isnan(dti_planarity)] = 0
dti_sphericity[np.isnan(dti_sphericity)] = 0
save_nifti(output_V1, evecs[:, :, :, :, 0], ras_img.affine, ras_img.header)
save_nifti(output_V2, evecs[:, :, :, :, 1], ras_img.affine, ras_img.header)
save_nifti(output_V3, evecs[:, :, :, :, 2], ras_img.affine, ras_img.header)
save_nifti(output_L1, evals[:, :, :, 0], ras_img.affine, ras_img.header)
save_nifti(output_L2, evals[:, :, :, 1], ras_img.affine, ras_img.header)
save_nifti(output_L3, evals[:, :, :, 2], ras_img.affine, ras_img.header)
save_nifti(output_fa, fa, ras_img.affine, ras_img.header)
save_nifti(output_color_fa, color_fa, ras_img.affine, ras_img.header)
save_nifti(output_md, md, ras_img.affine, ras_img.header)
save_nifti(output_ad, ad, ras_img.affine, ras_img.header)
save_nifti(output_rd, rd, ras_img.affine, ras_img.header)
save_nifti(output_ga, ga, ras_img.affine, ras_img.header)
save_nifti(output_tr, trace, ras_img.affine, ras_img.header)
save_nifti(output_PL, dti_planarity, ras_img.affine, ras_img.header)
save_nifti(output_SP, dti_sphericity, ras_img.affine, ras_img.header)
save_nifti(output_MO, dti_mode, ras_img.affine, ras_img.header)
save_nifti(output_res, residuals, ras_img.affine, ras_img.header)
#Reorient back to the original
output_imgs.append(output_tensor_nifti)
output_imgs.append(output_tensor_fsl)
output_imgs.append(output_tensor_mrtrix)
output_imgs.append(output_V1)
output_imgs.append(output_V2)
output_imgs.append(output_V3)
output_imgs.append(output_L1)
output_imgs.append(output_L2)
output_imgs.append(output_L3)
output_imgs.append(output_fa)
output_imgs.append(output_md)
output_imgs.append(output_rd)
output_imgs.append(output_ad)
output_imgs.append(output_ga)
output_imgs.append(output_color_fa)
output_imgs.append(output_PL)
output_imgs.append(output_SP)
output_imgs.append(output_MO)
output_imgs.append(output_res)
#Change orientation back to the original orientation
orig_ornt = nib.io_orientation(ras_img.affine)
targ_ornt = nib.io_orientation(img.affine)
transform = nib.orientations.ornt_transform(orig_ornt, targ_ornt)
affine_xfm = nib.orientations.inv_ornt_aff(transform, ras_img.shape)
trans_mat = affine_xfm[0:3, 0:3]
for img_path in output_imgs:
orig_img = nib.load(img_path)
reoriented = orig_img.as_reoriented(transform)
reoriented.to_filename(img_path)
#Correct FSL tensor for orientation
dirs = []
dirs.append(np.array([[1], [0], [0]]))
dirs.append(np.array([[1], [1], [0]]))
dirs.append(np.array([[1], [0], [1]]))
dirs.append(np.array([[0], [1], [0]]))
dirs.append(np.array([[0], [1], [1]]))
dirs.append(np.array([[0], [0], [1]]))
tensor_fsl = nib.load(output_tensor_fsl)
corr_fsl_tensor = np.empty(tensor_fsl.get_data().shape)
for i in range(0, len(dirs)):
rot_dir = np.matmul(trans_mat, dirs[i])
sign = 1.0
if np.sum(rot_dir) == 0.0:
sign = -1.0
if (np.absolute(rot_dir) == np.array([[1], [0], [0]])).all():
tensor_ind = 0
elif (np.absolute(rot_dir) == np.array([[1], [1], [0]])).all():
tensor_ind = 1
elif (np.absolute(rot_dir) == np.array([[1], [0], [1]])).all():
tensor_ind = 2
elif (np.absolute(rot_dir) == np.array([[0], [1], [0]])).all():
tensor_ind = 3
elif (np.absolute(rot_dir) == np.array([[0], [1], [1]])).all():
tensor_ind = 4
elif (np.absolute(rot_dir) == np.array([[0], [0], [1]])).all():
tensor_ind = 5
corr_fsl_tensor[:, :, :,
i] = sign * tensor_fsl.get_data()[:, :, :, tensor_ind]
save_nifti(output_tensor_fsl, corr_fsl_tensor, tensor_fsl.affine,
tensor_fsl.header)
#Now correct the eigenvectors
#Determine the order to rearrange
vec_order = np.transpose(targ_ornt[:, 0]).astype(int)
sign_order = np.transpose(targ_ornt[:, 1]).astype(int)
fsl_v1 = nib.load(output_V1)
corr_fsl_v1 = fsl_v1.get_data()[:, :, :, vec_order]
for i in range(0, 2):
corr_fsl_v1[:, :, :, i] = sign_order[i] * corr_fsl_v1[:, :, :, i]
save_nifti(output_FSL_V1, corr_fsl_v1, fsl_v1.affine, fsl_v1.header)
fsl_v2 = nib.load(output_V2)
corr_fsl_v2 = fsl_v2.get_data()[:, :, :, vec_order]
for i in range(0, 2):
corr_fsl_v2[:, :, :, i] = sign_order[i] * corr_fsl_v2[:, :, :, i]
save_nifti(output_FSL_V2, corr_fsl_v2, fsl_v2.affine, fsl_v2.header)
fsl_v3 = nib.load(output_V3)
corr_fsl_v3 = fsl_v3.get_data()[:, :, :, vec_order]
for i in range(0, 2):
corr_fsl_v3[:, :, :, i] = sign_order[i] * corr_fsl_v3[:, :, :, i]
save_nifti(output_FSL_V3, corr_fsl_v3, fsl_v3.affine, fsl_v3.header)
data = img.get_data()
affine = img.get_affine()
# Get the optional params:
fit_method = metadata.get('fit_method', "WLS")
fmask = metadata.get('fmask', None)
if fmask is None:
mask = None
else:
mask = nib.load(fmask).get_data().astype(bool)
# Fit the model:
tenmodel = dti.TensorModel(gtab, fit_method=fit_method)
tenfit = tenmodel.fit(data, mask=mask)
# Extract the nifti convention parameters:
lower_triangular = tenfit.lower_triangular()
lower_triangular = lower_triangular.astype('float32')
tensor_img = nifti1_symmat(lower_triangular, affine)
# The output will all have the same basic name as the data file-name:
root = op.join("/output",
op.splitext(op.splitext(op.split(fdata)[-1])[0])[0])
# Save to file:
dpsave(tensor_img, root + '_tensor.nii.gz')
for arr, label in zip([tenfit.ad, tenfit.rd, tenfit.md, tenfit.fa],
["_ad", "_rd", "_md", "_fa"]):
dpsave(nib.Nifti1Image(arr.astype("float32"), affine),
op.join('/output/', root + '%s.nii.gz'%label))
